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    <titleInfo>
        <title>The Effect of Context on Metaphor Paraphrase Aptness Judgments</title>
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        <namePart type="given">Yuri</namePart>
        <namePart type="family">Bizzoni</namePart>
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    <name type="personal">
        <namePart type="given">Shalom</namePart>
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        <dateIssued>2019-may</dateIssued>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>We conduct two experiments to study the effect of context on metaphor paraphrase aptness judgments. The first is an AMT crowd source task in which speakers rank metaphor-paraphrase candidate sentence pairs in short document contexts for paraphrase aptness. In the second we train a composite DNN to predict these human judgments, first in binary classifier mode, and then as gradient ratings. We found that for both mean human judgments and our DNN’s predictions, adding document context compresses the aptness scores towards the center of the scale, raising low out-of-context ratings and decreasing high out-of-context scores. We offer a provisional explanation for this compression effect.</abstract>
    <identifier type="citekey">bizzoni-lappin-2019-effect</identifier>
    <identifier type="doi">10.18653/v1/W19-0414</identifier>
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        <url>https://aclanthology.org/W19-0414</url>
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        <date>2019-may</date>
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            <start>165</start>
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%0 Conference Proceedings
%T The Effect of Context on Metaphor Paraphrase Aptness Judgments
%A Bizzoni, Yuri
%A Lappin, Shalom
%S Proceedings of the 13th International Conference on Computational Semantics - Long Papers
%D 2019
%8 may
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F bizzoni-lappin-2019-effect
%X We conduct two experiments to study the effect of context on metaphor paraphrase aptness judgments. The first is an AMT crowd source task in which speakers rank metaphor-paraphrase candidate sentence pairs in short document contexts for paraphrase aptness. In the second we train a composite DNN to predict these human judgments, first in binary classifier mode, and then as gradient ratings. We found that for both mean human judgments and our DNN’s predictions, adding document context compresses the aptness scores towards the center of the scale, raising low out-of-context ratings and decreasing high out-of-context scores. We offer a provisional explanation for this compression effect.
%R 10.18653/v1/W19-0414
%U https://aclanthology.org/W19-0414
%U https://doi.org/10.18653/v1/W19-0414
%P 165-175

Markdown (Informal)

[The Effect of Context on Metaphor Paraphrase Aptness Judgments](https://aclanthology.org/W19-0414) (Bizzoni & Lappin, 2019)

• The Effect of Context on Metaphor Paraphrase Aptness Judgments (Bizzoni & Lappin, 2019)

ACL

• Yuri Bizzoni and Shalom Lappin. 2019. The Effect of Context on Metaphor Paraphrase Aptness Judgments. In Proceedings of the 13th International Conference on Computational Semantics - Long Papers, pages 165–175, Gothenburg, Sweden. Association for Computational Linguistics.